Чем clusterdata отличается от k-means?

Здравствуйте. Чем clusterdata отличается от k-means?

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Сообщение от Prutvel Здравствуйте. Чем clusterdata отличается от k-means?

clusterdata - это иерархическая классификация, по которой далее можно построить иерархию классов
а k-means - это НЕ иерархическая классификация

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как в матлабе правильно задаётся clusterdata?

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help clusterdata:

clusterdata Construct clusters from data.

T = clusterdata(X, CUTOFF) constructs clusters from data X.
X is a matrix of size M by N, treated as M observations of N
variables. CUTOFF is a threshold for cutting the hierarchical
tree generated by LINKAGE into clusters. When 0 < CUTOFF < 2,
clusters are formed when inconsistent values are greater than
CUTOFF (see INCONSISTENT). When CUTOFF is an integer and CUTOFF >= 2,
then CUTOFF is considered as the maximum number of clusters to
keep in the hierarchical tree generated by LINKAGE. The output T is
a vector of size M containing a cluster number for each observation.

When 0 < CUTOFF < 2, T = clusterdata(X,CUTOFF) is equivalent to:
When CUTOFF is an integer >= 2, T = clusterdata(X,CUTOFF) is equivalent
to:

T = clusterdata(X,'PARAM1',VAL1,'PARAM2',VAL 2,...) provides more
control over the clustering through a set of parameter/value pairs.
Valid parameters are the following:

Parameter Value
'distance' Any of the distance metric names allowed by PDIST
(the 'minkowski' option can be followed by the
value of the exponent P).
'linkage' Any of the linkage methods allowed by LINKAGE.
'savememory' A string 'on' or 'off'. When the value is 'on',
clusterdata constructs clusters without computing the
distance matrix internally, so it usually requires
less memory for data with a large number of
observations. Value 'on' requires both of the
following two conditions to be satisfied:
* 'linkage' is 'ward', 'centroid', or 'median'.
* 'distance' is 'euclidean'.
When the above two conditions are satisfied, the
default is 'on' when the number of columns in X is
not greater than 20, or the machine doesn't have
enough memory to save the distance matrix.
'cutoff' Cutoff for inconsistent or distance measure.
'maxclust' Maximum number of clusters to form.
'criterion' Either 'inconsistent' or 'distance'.
'depth' Depth for computing inconsistent values.

Examples:

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